A SARS-CoV-2 - host proximity interactome (preprint)

Viral replication is dependent on interactions between viral polypeptides and host proteins. Identifying virus-host protein interactions can thus uncover unique opportunities for interfering with the virus life cycle via novel drug compounds or drug repurposing. Importantly, many viral-host protein interactions take place at intracellular membranes and poorly soluble organelles, which are difficult to profile using classical biochemical purification approaches. Applying proximity-dependent biotinylation (BioID) with the fast-acting miniTurbo enzyme to 27 SARS-CoV-2 proteins in a lung adenocarcinoma cell line (A549), we detected 7810 proximity interactions (7382 of which are new for SARS-CoV-2) with 2242 host proteins (results available at covid19interactome.org). These results complement and dramatically expand upon recent affinity purification-based studies identifying stable host-virus protein complexes, and offer an unparalleled view of membrane-associated processes critical for viral production. Host cell organellar markers were also subjected to BioID in parallel, allowing us to propose modes of action for several viral proteins in the context of host proteome remodelling. In summary, our dataset identifies numerous high confidence proximity partners for SARS-CoV-2 viral proteins, and describes potential mechanisms for their effects on specific host cell functions.

Clustering analysis of single nucleotide polymorphism data reveals population structure of SARS-CoV-2 worldwide (preprint)

The SARS-CoV-2 virus has been spreading rapidly and across the globe since first being reported in December 2019. To understand the evolutionary trajectory of the coronavirus, phylogenetic analysis is needed to study the population structure of SARS-CoV-2. As sequencing data worldwide is accruing rapidly, grouping them into clusters helps to organize the landscape of population structures. To effectively group these data, computational methodologies are needed to provide more productive and robust solutions for clustering. In this study, using the single nucleotide polymorphisms of the viral sequences as input features, we utilized three clustering algorithms, namely K-means, hierarchical clustering and balanced iterative reducing and clustering using hierarchies to partition the viral sequences into six major clusters. Comparison of the three clustering results reveals that the three methods produced highly consistent results, but K-means performed best and produced the smallest intra-cluster pairwise genetic distances among the three methods. The partition of the viral sequences revealed that the six clusters differed in their geographical distributions. Using comprehensive approaches to compare the diversity and selective pressure across the clusters, we discovered a high genetic diversity between the clusters. Based on characteristics of the mutation profiles in each cluster along with their geographical distributions and evolutionary histories, we identified the extent of molecular divergence within and between the clusters. The identification of the mutations that are strongly associated with clusters have potential implications for diagnosis and pathogenesis of COVID-19. In addition, the clustering method will enable further study of variant population structures in specific regions of these fast-growing viruses.

Multimerization- and glycosylation-dependent receptor binding of SARS-CoV-2 spike proteins (preprint)

Receptor binding studies using recombinant SARS-CoV proteins have been hampered due to challenges in approaches creating spike protein or domains thereof, that recapitulate receptor binding properties of native viruses. We hypothesized that trimeric RBD proteins would be suitable candidates to study receptor binding properties of SARS-CoV-1 and -2. Here we created monomeric and trimeric fluorescent RBD proteins, derived from adherent HEK293T, as well as in GnTI mutant cells, to analyze the effect of complex vs high mannose glycosylation on receptor binding. The results demonstrate that trimeric fully glycosylated proteins are superior in receptor binding compared to monomeric and immaturely glycosylated variants. Although differences in binding to commonly used cell lines were minimal between the different RBD preparations, substantial differences were observed when respiratory tissues of experimental animals were stained. The RBD trimers demonstrated distinct ACE2 expression profiles in bronchiolar ducts and confirmed the higher binding affinity of SARS-CoV-2 over SARS-CoV-1. Our results show that fully glycosylated trimeric RBD proteins are attractive to analyze receptor binding and explore ACE2 expression profiles in tissues.